Hello-
I understand that it's convention, when comparing two models using the
anova function anova(model1, model2), to put the more "complicated" (for
want of a better word) model as the second model. However, I'm using lme
in the nlme package and I've found that the order of the models actually
gives opposite results. I'm not sure if this is supposed to be the case
or if I have missed something important, and I can't find anything in
the Pinheiro and Bates book or in ?anova, or in Google for that matter
which unfortunately only returns results about ANOVA which isn't much
help. I'm using the latest version of R and nlme, just checked both.
Here is the code and output:
> PHQmodel1=lme(PHQ~Age+Gender+Date*Treatment, data=compfinal,
random=~1|Case, na.action=na.omit)
>
> PHQmodel2=lme(PHQ~Age+Gender+Date*Treatment, data=compfinal,
random=~1|Case, na.action=na.omit,
+ correlation=corAR1(form=~Date|Case))
> anova(PHQmodel1, PHQmodel2) # accept model 2
Model df AIC BIC logLik Test
L.Ratio p-value
PHQmodel1 1 8 48784.57 48840.43 -24384.28
PHQmodel2 2 9 48284.68 48347.51 -24133.34 1 vs 2 501.8926 <.0001
> PHQmodel1=lme(PHQ~Age+Gender+Date*Treatment, data=compfinal,
random=~1|Case, na.action=na.omit,
+ correlation=corAR1(form=~Date|Case))
>
> PHQmodel2=lme(PHQ~Age+Gender+Date*Treatment, data=compfinal,
random=~1|Case, na.action=na.omit)
> anova(PHQmodel1, PHQmodel2) # accept model 2
Model df AIC BIC logLik Test
L.Ratio p-value
PHQmodel1 1 9 48284.68 48347.51 -24133.34
PHQmodel2 2 8 48784.57 48840.43 -24384.28 1 vs 2 501.8926 <.0001
In both cases I am led to accept model 2 even though they are opposite
models. Is it really just that you have to put them in the right order?
It just seems like if there were say four models you wouldn't
necessarily be able to determine the correct order.
Many thanks,
Chris Beeley, Institute of Mental Health, UK
...session info follows
> sessionInfo()
R version 2.15.0 (2012-03-30)
Platform: i386-pc-mingw32/i386 (32-bit)
locale:
[1] LC_COLLATE=English_United Kingdom.1252 LC_CTYPE=English_United
Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252
attached base packages:
[1] grid stats graphics grDevices utils datasets
methods base
other attached packages:
[1] gridExtra_0.9 RColorBrewer_1.0-5 car_2.0-12
nnet_7.3-1 MASS_7.3-17
[6] xtable_1.7-0 psych_1.2.4 languageR_1.4
nlme_3.1-104 ggplot2_0.9.1
loaded via a namespace (and not attached):
[1] colorspace_1.1-1 dichromat_1.2-4 digest_0.5.2
labeling_0.1 lattice_0.20-6 memoise_0.1
[7] munsell_0.3 plyr_1.7.1 proto_0.3-9.2
reshape2_1.2.1 scales_0.2.1 stringr_0.6
[13] tools_2.15.0
> packageDescription("nlme")
Package: nlme
Version: 3.1-104
Date: 2012-05-21
Priority: recommended
Title: Linear and Nonlinear Mixed Effects Models
Authors@R: c(person("Jose", "Pinheiro", comment = "S version"),
person("Douglas", "Bates", comment =
"up to 2007"), person("Saikat", "DebRoy", comment = "up to
2002"), person("Deepayan",
"Sarkar", comment = "up to 2005"), person("R-core", email =
"r-c...@r-project.org", role =
c("aut", "cre")))
Author: Jose Pinheiro (S version), Douglas Bates (up to 2007), Saikat
DebRoy (up to 2002), Deepayan
Sarkar (up to 2005), the R Core team.
Maintainer: R-core <r-c...@r-project.org>
Description: Fit and compare Gaussian linear and nonlinear mixed-effects
models.
Depends: graphics, stats, R (>= 2.13)
Imports: lattice
Suggests: Hmisc, MASS
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
BugReports: http://bugs.r-project.org
Packaged: 2012-05-23 07:28:59 UTC; ripley
Repository: CRAN
Date/Publication: 2012-05-23 07:37:45
Built: R 2.15.0; x86_64-pc-mingw32; 2012-05-29 12:36:01 UTC; windows
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